Statistical Monitoring for NVM Write

Emerging non-volatile memory (NVM) technologies promise persistence, byte-addressability, low power consumption and high density. The potential of NVM implies that it can improve persistence performance and availability for high-performance computing tasks in data centers. However, NVM suffers from limited write endurance; skewed writes can extremely curtail the lifetime of NVM. Although several wear-leveling algorithms have been proposed to address the wear-out problem, based on estimation of write counts, they come with limitations in accuracy and compatibility with existing hardware and softwares. In this paper, we propose several software-based ways of monitoring NVM writes and obtaining the write counts, which are more accurate and non-intrusive for existing programs. We compare our proposed monitoring approaches in the aspects of granularity and portability; we conduct extensive experiments to evaluate them and the results show that our proposed methods can obtain good accuracy with an affordable time overhead.